Sandra Real: Getting to Know the Women in Data Science

15th May, 2018

Sandra Real is a Data Engineer Team Lead at STRANDS. Her career has revolved around the analytics industry and the development and use of data technologies for more than fourteen years, and she is a firm believer that there’s nothing more dynamite than data.

With a focus on Oracle Databases, Sandra’s expertise in data architecture, administration, and implementation has grown significantly as DBA at Hewlett Packard, NexTReT and TIPSA. She’s a programmer and all-round dataholic.

But that’s not all. Sandra is refreshingly honest, mother to a three-year-old son, and a total tour de force. Here’s her story:

1. How did you become interested in Data Analytics and Technology?

I started by pursuing a bachelor's degree in nursing many years ago, but it didn’t take me long to realize that nursing wasn’t for me. I loved spending time in the hospital and learning about the patients’ conditions, but I found dealing with the day-to-day realities of a career in medicine a bit much to cope with.

I jumped into another field of science I was passionate about: data. I became a DBA in the education and financial spaces, and began to perform a wide range of related activities related to designing, implementing and maintaining database systems.

Then, there was one thing I couldn’t wait to get my hands on: data analysis. I’m still amazed at the kind of information available for businesses to use in decision-making, and technologies provide companies with the means to truly analyze and interpret vast amounts of data to make informed decisions.

2. Can you pinpoint one moment or person that was instrumental in your decision to pick this career path?

It was actually my neighbor.

I was about 20 years old when I became obsessed with experimenting with my new computer. I installed some things, uninstalled others, and basically used it so much that it was constantly in need of repair.

As luck would have it, my neighbor was really good with computers. I was always knocking on his door, asking for help. Until one day I decided I wouldn’t ask him anymore. I wanted to know how those things worked for myself, so I did a little bit of digging and learned to fix the computer on my own.

That experience actually enhanced my interest in all things technology - which I’m very grateful for.

3. How did you come to join STRANDS, and can you tell us about some initiatives you lead here?

I was referred for a Data Developer position by a co-worker at that time. And frankly, my first thought was that I wasn’t prepared for the job. I didn’t have a background in development, and my financial experience was more production-based.

Despite my concerns, I decided to go for it and face the challenges that came with the role. That was the best possible training I could have asked for.

Obviously I am biased, but I love the skill-set that the data team brings to the table at STRANDS.

We’re fully aware of the consequences that good or bad database management can have in all environments, especially in production. This is critical when it comes to troubleshooting and correcting problems.

One of the projects with greatest impact we’ve worked on as a team and that I’m most proud of is that of data categorization, which is the foundation of our financial management products.

4. What are your favorite books, websites, films, and/or resources in the industry?

KDnuggets. A site founded by Gregory Piatetsky-Shapiro covering all things AI, Analytics, Big Data, Machine Learning, you name it.

Kaggle. Very cool place to participate in Data Science competitions and test your skills.

Moneyball. A movie based on a true story about a baseball manager that employs computer-generated analysis to acquire new players.

5. What advice do you have for anyone interested in a career in Data Science?

I’d say: Don't wait until you feel ready to take action.

That applies to everything, but it’s particularly true in the Data Science profession. This sector is so dynamic and involves so many disciplines (methods, processes, algorithms, systems...) that you will never know it all. And that’s okay - as long as you feed your motivation and keep learning.

If you have any inclination that a career in Data Science is the kind of work you would enjoy and that you would be good at, you should definitely explore it.